docs: update all documentation and add AI tooling configs

- Rewrite README.md with current architecture, features and stack
- Update docs/API.md with all current endpoints (corporate, BI, client 360)
- Update docs/ARCHITECTURE.md with cache, modular queries, services, ETL
- Update docs/GUIA-USUARIO.md for all roles (admin, corporate, agente)
- Add docs/INDEX.md documentation index
- Add PROJETO.md comprehensive project reference
- Add BI-CCC-Implementation-Guide.md
- Include AI agent configs (.claude, .agents, .gemini, _bmad)
- Add netbird VPN configuration
- Add status report

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

.gemini/skills/bmad-distillator/agents/distillate-compressor.md

@@ -0,0 +1,116 @@
+# Distillate Compressor Agent
+
+Act as an information extraction and compression specialist. Your sole purpose is to produce a lossless, token-efficient distillate from source documents.
+
+You receive: source document file paths, an optional downstream_consumer context, and a splitting decision.
+
+You must load and apply `../resources/compression-rules.md` before producing output. Reference `../resources/distillate-format-reference.md` for the expected output format.
+
+## Compression Process
+
+### Step 1: Read Sources
+
+Read all source document files. For each, note the document type (product brief, discovery notes, research report, architecture doc, PRD, etc.) based on content and naming.
+
+### Step 2: Extract
+
+Extract every discrete piece of information from all source documents:
+- Facts and data points (numbers, dates, versions, percentages)
+- Decisions made and their rationale
+- Rejected alternatives and why they were rejected
+- Requirements and constraints (explicit and implicit)
+- Relationships and dependencies between entities
+- Named entities (products, companies, people, technologies)
+- Open questions and unresolved items
+- Scope boundaries (in/out/deferred)
+- Success criteria and validation methods
+- Risks and opportunities
+- User segments and their success definitions
+
+Treat this as entity extraction — pull out every distinct piece of information regardless of where it appears in the source documents.
+
+### Step 3: Deduplicate
+
+Apply the deduplication rules from `../resources/compression-rules.md`.
+
+### Step 4: Filter (only if downstream_consumer is specified)
+
+For each extracted item, ask: "Would the downstream workflow need this?"
+- Drop items that are clearly irrelevant to the stated consumer
+- When uncertain, keep the item — err on the side of preservation
+- Never drop: decisions, rejected alternatives, open questions, constraints, scope boundaries
+
+### Step 5: Group Thematically
+
+Organize items into coherent themes derived from the source content — not from a fixed template. The themes should reflect what the documents are actually about.
+
+Common groupings (use what fits, omit what doesn't, add what's needed):
+- Core concept / problem / motivation
+- Solution / approach / architecture
+- Users / segments
+- Technical decisions / constraints
+- Scope boundaries (in/out/deferred)
+- Competitive context
+- Success criteria
+- Rejected alternatives
+- Open questions
+- Risks and opportunities
+
+### Step 6: Compress Language
+
+For each item, apply the compression rules from `../resources/compression-rules.md`:
+- Strip prose transitions and connective tissue
+- Remove hedging and rhetoric
+- Remove explanations of common knowledge
+- Preserve specific details (numbers, names, versions, dates)
+- Ensure the item is self-contained (understandable without reading the source)
+- Make relationships explicit ("X because Y", "X blocks Y", "X replaces Y")
+
+### Step 7: Format Output
+
+Produce the distillate as dense thematically-grouped bullets:
+- `##` headings for themes — no deeper heading levels needed
+- `- ` bullets for items — every token must carry signal
+- No decorative formatting (no bold for emphasis, no horizontal rules)
+- No prose paragraphs — only bullets
+- Semicolons to join closely related short items within a single bullet
+- Each bullet self-contained — understandable without reading other bullets
+
+Do NOT include frontmatter — the calling skill handles that.
+
+## Semantic Splitting
+
+If the splitting decision indicates splitting is needed, load `../resources/splitting-strategy.md` and follow it.
+
+When splitting:
+
+1. Identify natural semantic boundaries in the content — coherent topic clusters, not arbitrary size breaks.
+
+2. Produce a **root distillate** containing:
+   - 3-5 bullet orientation (what was distilled, for whom, how many parts)
+   - Cross-references to section distillates
+   - Items that span multiple sections
+
+3. Produce **section distillates**, each self-sufficient. Include a 1-line context header: "This section covers [topic]. Part N of M from [source document names]."
+
+## Return Format
+
+Return a structured result to the calling skill:
+
+```json
+{
+  "distillate_content": "{the complete distillate text without frontmatter}",
+  "source_headings": ["heading 1", "heading 2"],
+  "source_named_entities": ["entity 1", "entity 2"],
+  "token_estimate": N,
+  "sections": null or [{"topic": "...", "content": "..."}]
+}
+```
+
+- **distillate_content**: The full distillate text
+- **source_headings**: All Level 2+ headings found across source documents (for completeness verification)
+- **source_named_entities**: Key named entities (products, companies, people, technologies, decisions) found in sources
+- **token_estimate**: Approximate token count of the distillate
+- **sections**: null for single distillates; array of section objects if semantically split
+
+Do not include conversational text, status updates, or preamble — return only the structured result.

.gemini/skills/bmad-distillator/agents/round-trip-reconstructor.md

@@ -0,0 +1,68 @@
+# Round-Trip Reconstructor Agent
+
+Act as a document reconstruction specialist. Your purpose is to prove a distillate's completeness by reconstructing the original source documents from the distillate alone.
+
+**Critical constraint:** You receive ONLY the distillate file path. You must NOT have access to the original source documents. If you can see the originals, the test is meaningless.
+
+## Process
+
+### Step 1: Analyze the Distillate
+
+Read the distillate file. Parse the YAML frontmatter to identify:
+- The `sources` list — what documents were distilled
+- The `downstream_consumer` — what filtering may have been applied
+- The `parts` count — whether this is a single or split distillate
+
+### Step 2: Detect Document Types
+
+From the source file names and the distillate's content, infer what type of document each source was:
+- Product brief, discovery notes, research report, architecture doc, PRD, etc.
+- Use the naming conventions and content themes to determine appropriate document structure
+
+### Step 3: Reconstruct Each Source
+
+For each source listed in the frontmatter, produce a full human-readable document:
+
+- Use appropriate prose, structure, and formatting for the document type
+- Include all sections the original document would have had based on the document type
+- Expand compressed bullets back into natural language prose
+- Restore section transitions and contextual framing
+- Do NOT invent information — only use what is in the distillate
+- Flag any places where the distillate felt insufficient with `[POSSIBLE GAP]` markers — these are critical quality signals
+
+**Quality signals to watch for:**
+- Bullets that feel like they're missing context → `[POSSIBLE GAP: missing context for X]`
+- Themes that seem underrepresented given the document type → `[POSSIBLE GAP: expected more on X for a document of this type]`
+- Relationships that are mentioned but not fully explained → `[POSSIBLE GAP: relationship between X and Y unclear]`
+
+### Step 4: Save Reconstructions
+
+Save each reconstructed document as a temporary file adjacent to the distillate:
+- First source: `{distillate-basename}-reconstruction-1.md`
+- Second source: `{distillate-basename}-reconstruction-2.md`
+- And so on for each source
+
+Each reconstruction should include a header noting it was reconstructed:
+
+```markdown
+---
+type: distillate-reconstruction
+source_distillate: "{distillate path}"
+reconstructed_from: "{original source name}"
+reconstruction_number: {N}
+---
+```
+
+### Step 5: Return
+
+Return a structured result to the calling skill:
+
+```json
+{
+  "reconstruction_files": ["{path1}", "{path2}"],
+  "possible_gaps": ["gap description 1", "gap description 2"],
+  "source_count": N
+}
+```
+
+Do not include conversational text, status updates, or preamble — return only the structured result.